README.md

metadata

language:
  - en
library_name: diffusers
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
base_model:
  - black-forest-labs/FLUX.1-dev
  - black-forest-labs/FLUX.1-schnell
  - ByteDance/Hyper-SD
base_model_relation: merge

FLUX.1-merged_uncensored

This repository provides the merged params for black-forest-labs/FLUX.1-dev and black-forest-labs/FLUX.1-schnell originally provided by @sayakpaul.

The base model was then fused with a selection of LoRAs, some of which are NSFW in nature. Please use responsibily. Please be aware of the licenses of both the models before using the params commercially.

This model was created as part of the ongoing OpenSight project, mostly for dataset generation and evaluation purposes.

The following context provided by sayakpaul, of the original merged model.

Dev (50 steps)	Dev (4 steps)	Dev + Schnell (4 steps)

Sub-memory-efficient merging code

from diffusers import FluxTransformer2DModel
from huggingface_hub import snapshot_download
from accelerate import init_empty_weights
from diffusers.models.model_loading_utils import load_model_dict_into_meta
import safetensors.torch
import glob
import torch


with init_empty_weights():
    config = FluxTransformer2DModel.load_config("black-forest-labs/FLUX.1-dev", subfolder="transformer")
    model = FluxTransformer2DModel.from_config(config)

dev_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-dev", allow_patterns="transformer/*")
schnell_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-schnell", allow_patterns="transformer/*")

dev_shards = sorted(glob.glob(f"{dev_ckpt}/transformer/*.safetensors"))
schnell_shards = sorted(glob.glob(f"{schnell_ckpt}/transformer/*.safetensors"))

merged_state_dict = {}
guidance_state_dict = {}

for i in range(len((dev_shards))):
    state_dict_dev_temp = safetensors.torch.load_file(dev_shards[i])
    state_dict_schnell_temp = safetensors.torch.load_file(schnell_shards[i])

    keys = list(state_dict_dev_temp.keys())
    for k in keys:
        if "guidance" not in k:
            merged_state_dict[k] = (state_dict_dev_temp.pop(k) + state_dict_schnell_temp.pop(k)) / 2
        else:
            guidance_state_dict[k] = state_dict_dev_temp.pop(k)

    if len(state_dict_dev_temp) > 0:
        raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.")
    if len(state_dict_schnell_temp) > 0:
        raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.")

merged_state_dict.update(guidance_state_dict)
load_model_dict_into_meta(model, merged_state_dict)

model.to(torch.bfloat16).save_pretrained("merged-flux")